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Oracle 1z0-1127-24 Exam Syllabus Topics:

Topic
Details

Topic 1

Building an LLM Application with OCI Generative AI Service: For AI Engineers, this section covers Retrieval Augmented Generation (RAG) concepts, vector database concepts, and semantic search concepts. It also focuses on deploying an LLM, tracing and evaluating an LLM, and building an LLM application with RAG and LangChain.

Topic 2

Using OCI Generative AI Service: For AI Specialists, this section covers dedicated AI clusters for fine-tuning and inference. The topic also focuses on the fundamentals of OCI Generative AI service, foundational models for Generation, Summarization, and Embedding.

Topic 3

Fundamentals of Large Language Models (LLMs): For AI developers and Cloud Architects, this topic discusses LLM architectures and LLM fine-tuning. Additionally, it focuses on prompts for LLMs and fundamentals of code models.

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Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions (Q36-Q41):

NEW QUESTION # 36
What issue might arise from using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service?

A. Overfilling
B. Underfitting
C. Data Leakage
D. Model Drift

Answer: B

Explanation:
Using small data sets with the Vanilla fine-tuning method in the OCI Generative AI service might result in underfitting. Underfitting occurs when a model is too simplistic to capture the underlying patterns in the data, leading to poor performance on both training and validation data. This is particularly problematic with small data sets because there may not be enough information for the model to learn the necessary patterns and relationships.
Reference
Articles on machine learning challenges with small data sets
Technical documentation on fine-tuning models in OCI

NEW QUESTION # 37
How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?

A. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
B. By optimizing GPIJ memory utilization for each model's unique para
C. By allocating separate GPUS for each model instance
D. By loading the entire model into G PU memory for efficient processing

Answer: A

Explanation:
The architecture of dedicated AI clusters contributes to minimizing GPU memory overhead for fine-tuned model inference by sharing base model weights across multiple fine-tuned models on the same group of GPUs. This approach allows different fine-tuned models to leverage the shared base model weights, reducing the memory requirements and enabling efficient use of GPU resources. By not duplicating the base model weights for each fine-tuned model, the system can handle more models simultaneously with lower memory overhead.
Reference
Technical documentation on AI cluster architectures
Research articles on optimizing GPU memory utilization in model inference

NEW QUESTION # 38
When should you use the T-Few fine-tuning method for training a model?

A. For data sets with hundreds of thousands to millions of samples
B. For data sets with a few thousand samples or less
C. For complicated semantical undemanding improvement
D. For models that require their own hosting dedicated Al duster

Answer: B

Explanation:
The T-Few fine-tuning method is particularly suitable for data sets with a few thousand samples or less. This method is designed to be efficient and effective with limited data, making it ideal for scenarios where collecting large amounts of training data is impractical. T-Few fine-tuning allows for meaningful adjustments to the model even with smaller data sets, providing good performance improvements without requiring extensive data.
Reference
Articles on fine-tuning techniques for small data sets
Technical documentation on T-Few fine-tuning in machine learning models

NEW QUESTION # 39
Which statement is true about Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT)?

A. Fine-tuning and PEFT do not involve model modification; they differ only in the type of data used for training, with Fine-tuning requiring labeled data and PEFT using unlabeled data.
B. Both Fine-tuning and PEFT require the model to be trained from scratch on new data, making them equally data and computationally intensive.
C. Fine-tuning requires training the entire model on new data, often leading to substantial computational costs, whereas PEFT involves updating only a small subset of parameters, minimizing computational requirements and data needs.
D. PEFT requires replacing the entire model architecture with a new one designed specifically for the new task, making it significantly more data-intensive than Fine-tuning.

Answer: C

Explanation:
Fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) are two techniques used for adapting pre-trained LLMs for specific tasks.
Fine-tuning:
Modifies all model parameters, requiring significant computing power.
Can lead to catastrophic forgetting, where the model loses prior general knowledge.
Example: Training GPT on medical texts to improve healthcare-specific knowledge.
Parameter-Efficient Fine-Tuning (PEFT):
Only a subset of model parameters is updated, making it computationally cheaper.
Uses techniques like LoRA (Low-Rank Adaptation) and Adapters to modify small parts of the model.
Avoids retraining the full model, maintaining general-purpose knowledge while adding task-specific expertise.
Why Other Options Are Incorrect:
(A) is incorrect because fine-tuning does not train from scratch, but modifies an existing model.
(B) is incorrect because both techniques involve model modifications.
(D) is incorrect because PEFT does not replace the model architecture.
🔹 Oracle Generative AI Reference:
Oracle AI supports both full fine-tuning and PEFT methods, optimizing AI models for cost efficiency and scalability.

NEW QUESTION # 40
An AI development company is working on an advanced AI assistant capable of handling queries in a seamless manner. Their goal is to create an assistant that can analyze images provided by users and generate descriptive text, as well as take text descriptions and produce accurate visual representations. Considering the capabilities, which type of model would the company likely focus on integrating into their AI assistant?

A. A diffusion model that specializes in producing complex outputs.
B. A language model that operates on a token-by-token output basis
C. A Retrieval Augmented Generation (RAG) model that uses text as input and output
D. A Large Language Model based agent that focuses on generating textual responses

Answer: A

Explanation:
An AI development company aiming to create an assistant capable of analyzing images and generating descriptive text, as well as converting text descriptions into accurate visual representations, would likely focus on integrating a diffusion model. Diffusion models are advanced generative models that specialize in producing complex outputs, including high-quality images from textual descriptions and vice versa.
Reference
Research papers on diffusion models and their applications
Technical documentation on generative models for image and text synthesis

NEW QUESTION # 41
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